The development of a framework to assess long-term water supply and demand projections for an integrated assessment of environment impacts for the energy sector
Bibliographic record
Abstract
This paper aims to develop a framework for assessing cross-sectoral water demand over a long-term planning horizon using a Water Evaluation and Planning (WEAP) model for the energy sector and integrating it with the assessment of greenhouse gas (GHG) emissions. This framework spatially associates surface and ground water supply resources with sectoral water demand between 2005 and 2050. The developed framework uses a hybrid top-down and bottom-up approach for linking water use in the municipal, commercial, oil and gas, power, industrial, and agriculture sectors with their water sources. Annual water use associated with future changes in population, oil sands production (in situ and surface mining), power generation fuels and technologies, and agricultural and livestock population is quantified. A case study for Alberta was conducted. Eleven future scenarios were evaluated, and model results show that the water demand might increase by 11–25% from the 2020 demand by 2050. The developed framework can be used to provide insights into patterns of water demand and supply for the energy sector as well as other sectors in different scenarios and can be integrated with assessments of GHG emissions, which can aid in decision-making at the provincial and national levels. This framework can be used for other jurisdictions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".